Much of our work is described in scientific or more public orientated publications. Where possible these papers/documents can be downloaded here.
Why We See What We Do: An Empirical Theory of Vision (Paperback)
Background: The perception of brightness depends on spatial context: the same stimulus can appear light or dark depending on what surrounds it. A less well-known but equally important contextual phenomenon is that the colour of a stimulus can also alter its brightness. Specifically, stimuli that are more saturated (i.e. purer in colour) appear brighter than stimuli that are less saturated at the same luminance. Similarly, stimuli that are red or blue appear brighter than equiluminant yellow and green stimuli. This non-linear relationship between stimulus intensity and brightness, called the Helmholtz-Kohlrausch (HK) effect, was first described in the nineteenth century but has never been explained. Here, we take advantage of the relative simplicity of this ‘illusion' to explain it and contextual effects more generally, by using a simple Bayesian ideal observer model of the human visual ecology. We also use fMRI brain scans to identify the neural correlates of brightness without changing the spatial context of the stimulus, which has complicated the interpretation of related fMRI studies.
Results: Rather than modelling human vision directly, we use a Bayesian ideal observer to model human visual ecology. We show that the HK effect is a result of encoding the non-linear statistical relationship between retinal images and natural scenes that would have been experienced by the human visual system in the past. We further show that the complexity of this relationship is due to the response functions of the cone photoreceptors, which themselves are thought to represent an efficient solution to encoding the statistics of images. Finally, we show that the locus of the response to the relationship between images and scenes lies in the primary visual cortex (V1), if not earlier in the visual system, since the brightness of colours (as opposed to their luminance) accords with activity in V1 as measured with fMRI.
Conclusions: The data suggest that perceptions of brightness represent a robust visual response to the likely sources of stimuli, as determined, in this instance, by the known statistical relationship between scenes and their retinal responses. While the responses of the early visual system (receptors in this case) may represent specifically the statistics of images, post receptor responses are more likely represent the statistical relationship between images and scenes. A corollary of this suggestion is that the visual cortex is adapted to relate the retinal image to behaviour given the statistics of its past interactions with the sources of retinal images: the visual cortex is adapted to the signals it receives from the eyes, and not directly to the world beyond.
Determining the statistical relationships of images that facilitate robust visual behaviour is nontrivial. Here we ask if some spatial relationships are more easily learned by the visual brain than others. Visually naïve bumblebees were trained to recognise coloured artificial flowers in scenes of equal spatial complexity but differing patterns of stimulus intensity. When flowers of similar intensity were grouped into extended regions across the array (coincident with natural patterns of light), the accuracy of the bees' foraging behaviour was dependent on spatial context, even though this information was redundant to the task. When the same intensity information was organised into a pattern that was less consistent with natural patterns of illumination but of equal order, their behaviour was independent of spatial context and they required double the training time to solve the same conditional task. These observations suggest the brain is biased to more efficiently encode/learn ecologically ‘meaningful' image correlations.
Fundamental to
understanding the evolution and function of biological systems is to comprehend
how such systems develop. Most consider the process of development to be a
wholly hierarchical genetically programmed process. Without dismissing the
importance of genetic regulation, here we consider the hypothesis that development,
at least of some systems, can be understood as an epigenetic
landscape (first proposed by Waddington, 1957), where the contours reflect the attractor states of the system's
regulatory network(s). We specifically study as a stochastic system the
network that underlies cell fate determination for floral organ primordia in Arabidopsis
thaliana, which is a key system in plant biology
research. Arabidopsis, like most
angiosperms, follows a stereotypical sequence in the spatial and temporal
differentiation of its floral primordia cells: first to sepals, then to petals,
then to stamens and ultimately to carpels. Also well-known are fifteen of the
genes that are involved in a complex regulatory network underlying this
developmental process. Here we propose a
method to reduce the dimensionality of genetic networks using both discrete and continuous stochastic models of cell populations,
which attain each of the four primordial states (sepals, petals, stamens and
carpels) with the same temporal sequence observed in Arabidopsis flower development. Thus our results suggest that such temporal
sequence is, at least in part, a robust emergent consequence of the
interactions in the gene network. Therefore, our results could also explain the
evolutionary conservation of angiosperm flower patterning despite the
contrasting conditions in which plants of diverse lineages develop. More
generally, the computational and conceptual framework presented here provides
the basis for linking genes, morphogenetic patterns and the evolution of any
dynamical system or network.
Artists and scientists have a perpetual interest in the relationship between music and art. As technology has progressed, so too have the tools that allow the practical exploration of this relationship. Today, artists in many disparate fields occupy themselves with producing animated visual art that is correlated with music (called ‘visual music'). Despite this interest and advancing technology, there still is no tool that will allow one to perform visual music in real-time with a significant level of control. Here we propose a system that would enable a group or individual to perform live ‘visual music' using the musical instrument(s) itself as the primary source of control information for the graphics.
Previous work suggests that innate immunity and representations of tissue can be useful when combined with artificial immune systems. Here we provide a new implementation of tissue for AIS using systemic computation, a new model of computation and corresponding computer architecture based on a systemics world-view and supplemented by the incorporation of natural characteristics. We show using systemic computation how to create an artificial organism, a program with metabolism that eats data, expels waste, clusters cells based on the nature of its food and emits danger signals suitable for an artificial immune system. The implementation is tested by application to a standard machine learning set and shows excellent abilities to recognise anomalies in its diet.
Reliability in
computer or engineering systems is undoubtedly a key requirement in the
development process. Safety within critical control systems, and reliable data
transfers, require tolerance to unexpected and unwanted phenomena. In biology,
new cells can replace damaged cells, DNA is able to repair and replicate with
error control. These processes are essential to maintain the overall organism.
Biology has often been a successful inspiration in computation (artificial
neural networks, genetic algorithms, ant colony optimisation, etc) although
conventional computation differs widely from natural computation. In this
respect, we have introduced systemic computation (SC), a model of interacting
systems with natural characteristics and suggested a new computer architecture.
Following this work, we then introduced a systemic computer as a virtual
machine running on conventional computers. In this paper we show, using a
genetic algorithm implementation running on this platform, how crash-proof
programs following the SC paradigm have native fault-tolerance and easily
integrated self-maintenance.
Here we describe a
programme that will enable the performance of advanced real-time computer
graphics by non-programmers through flexible modules that are easily exchanged and
managed.
Some images are
perceptually bi-stable because the image's global – or average – description is
ambiguous: when considered as a whole such images have multiple, equally
likely ‘interpretations' between which visual
consciousness seems to fluctuate. While these fluctuations are thought to be
random, here we consider whether in some instances they represent coherent
shifts in attention. To test this, subjects viewed an ‘X' moving behind an
aperture. When attention (and eye-movements) were unconstrained, subjects reported seemingly random fluctuations in their
conscious awareness between an ‘X' moving back-and-forth across the aperture
(pattern motion), and two bars moving in opposition along the aperture's
vertical axis (component motion). The timing and duration of their fluctuations
were unpredictable within and between subjects, thus demonstrating the inherent
bi-stability of the global stimulus. Subjects were then asked to attend to
local features of the X. When attending its centre – where the local motion
energy is vertical, they reported only pattern motion. Conversely, when
attending the end points of the ‘X' – where the local motion energy is
horizontal, they perceived only component motion. In this instance, then,
visual awareness of a globally ambiguous image is not random, but perfectly
predicted by the statistics of the attended element. What we see, then, is
determined by the current topology of a high-dimensional feature landscape in
the visual cortex, which itself represents, not only evolutionary and
developmental history, but also one's immediate attentional state.
Previous
research has shown that bees generate colour constancy by learning the spectral
relationships in scenes that were useful in the past. Here we consider whether
bees encode contextual relationships even in invariant environments where relational
information is redundant. Under these constrained conditions, bees still used
relational information to recognise the rewarding stimulus. However, the same
bees also remembered the rewarding stimulus in absolute terms, independent of
context. We therefore conclude that bumblebees can use and ignore seeming mutually exclusive visual
information simultaneously to generate behaviour, thus maximising robustness
(in line with their phylogenetic experience) and computational efficiency with
respect to the statistics of their extant visual environment.
While it is possible to
describe a code in great detail, deciphering it requires understanding the
nature of the information encoded. Similarly, explaining how we see what we do
requires not only quantitative descriptions of the visual brain's functional architecture,
but also a clear understanding of what is represented in that architecture.
Theoretical and computational neuroscience attempts to explain what that
information might be. Here I review the rationale and evidence for the
hypothesis that the brain encodes the statistics of natural images and the
behavioural significance of natural images in past visual experience.
An illusion is
the phenomenon of perceiving something different from what is physically there.
This chapter will describe why illusions may be a useful tool in the classroom
for learning how and why we see what we do, and consider how the exploration of
illusion encourages children's (and adults') curiosity, creativity and
confidence (the three ‘Cs'). The chapter is divided into three sections. The
first describes the science of seeing illusions: why we see them and what it tells us about how the brain
works. The second section – art of seeing – describes one example of applying this scientific understanding in
the context of an art project in a primary school classroom. The third section
entitled the illusion metaphor explains the importance of using illusions to break with some received
methods of learning and teaching by emphasising the ambiguity of learned and
inherited truths and conventions, with the implicit aim of encouraging children
to respond more empathetically to the world around them.
Bio-inspired processes are involved more and more in today's technologies, yet their modelling and implementation tend to be taken away from their original concept because of the limitations of the classical computation paradigm. To address this, we previously introduced systemic computation (SC), a model of interacting systems with natural characteristics, and further introduced a modeling platform with a bio-inspired system implementation. In this paper, we investigate the impact of local knowledge and asynchronous computation: signi?cant natural properties of biological neural networks (NN) and naturally handled by SC. We present here a bio-inspired model of arti?cial NN, focussing on agent interactions, and show that exploiting these built-in properties, which come for free, enables neural structure ?exibility without reducing performance.
Computation in biology and in conventional computer architectures seem to share some features, yet many of their important characteristics are very different. To address this, (Bentley 2007) introduced systemic computation, a model of interacting systems with natural characteristics. Following this work, here we introduce the first platform implementing such computation, including programming language, compiler and virtual machine. To investigate their use we then provide an implementation of a genetic algorithm applied to the travelling salesman problem and also explore how SC enables self-adaptation with the minimum of additional code.
A system composed of
multiple interacting components is capable of responding to contextual
information and can produce a higher range of non-linear responses to stimuli
compared to a modular system with a low degree of component interaction.
However, the fitness landscape of highly integrated systems is more rugged
indicating that such systems are likely to be less evolvable. In this work we
use an artificial life simulation to investigate whether the evolvability of
highly integrated systems can be improved if the level of integration between
the system's components is under evolutionary control. When evolving our
multi-component system we discover that the level of integration very quickly
falls to virtually zero reducing the ruggedness of the landscape and making it
nearly neutral. This allows the evolving population to explore the genome space
without getting stuck on local optima. The components then integrate and the
evolving population settles on the global optimum. This work is unique because
the presented problem requires the evolving system to be fully integrated in
order to solve it and as such the decreased ruggedness and near neutrality are
not a permanent feature of the landscape but rather a property which is
temporarily manipulated and exploited by the evolving population.
Lightness illusions are
fundamental to human perception, and yet why we see them is still the focus of much research. Here we address the
question by modelling, not human physiology or perception directly as is
typically the case, but our natural visual world and the need for robust
behaviour. Artificial neural networks were trained to predict the reflectance
of surfaces in a synthetic ecology consisting of 3-dimensional ‘dead-leaf'
scenes under non-uniform illumination. The networks learned to solve this task
accurately and robustly given only ambiguous sense data. In addition – and as a
direct consequence of their experience – the networks also made systematic
‘errors' in their behaviour commensurate with human illusions, which includes
brightness contrast and assimilation – although assimilation (specifically
White's illusion) only emerged when the virtual ecology included 3D, as opposed
to 2D scenes. Subtle variations in these illusions, also found in human
perception, were observed, such as the asymmetry of brightness contrast. These
data suggest that ‘illusions' arise in humans because (i) natural stimuli are
ambiguous, and (ii) this ambiguity is resolved empirically by encoding the
statistical relationship between images and scenes in past visual experience.
Since resolving stimulus ambiguity is a challenge faced by all visual systems,
a corollary of these findings is that human illusions must be experienced by
all visual animals regardless of their particular neural machinery. The data
also provide a more formal definition of illusion: the condition in which the
true source of a stimulus differs from what is its most likely (and thus
perceived) source. As such, illusions are not fundamentally different from
non-illusory percepts, all being direct manifestations of the statistical
relationship between images and scenes.
The Constraint
Satisfaction Problem (CSP) is one of the most prominent problems in artificial
intelligence, logic, theoretical computer science, engineering and many other
areas in science and industry. One instance of a CSP, the satisfiability problem
in propositional logic (SAT), has become increasingly popular and has
illuminated important insights into our understanding of the fundamentals of
computation. Though the concept of representing propositional formulae as n-partite
graphs is certainly not novel, in this paper we introduce a new polynomial
reduction from 3SAT to Gn7 graphs and demonstrate that this framework has
advantages over the standard representation. More specifically, after
presenting the reduction we show that many hard 3SAT instances represented in
this framework can be solved using a basic arc-consistency algorithm, and
finally we discuss the potential advantages and implications of using such a
representation.
Slater,M., Frisoli,A., Tecchia,F., Guger,C., Lotto,B., Steed,A., Pfurtscheller,C., Leeb,R., Reiner,M., Sanchez-Vives,M.V., Bernardet,U., Verschure,P. (2007). Understanding and Realizing Presence in the Presenccia Project. IEEE Computer Graphics and Applications 27(4), 90-93. ISSN: 0272-1716
This paper expands
Mosaic World, an artificial life model, in order to directly test theories on
the emergence of multicellular life. Five experiments are conducted and
demonstrate that both the presence of predation and accidental aggregation are
sufficient conditions for the transition to multicellularity. In addition, it
is shown that division of labour is a major benefit for aggregation, and
evolves even if aggregates ‘pay' for abilities they do not use. Analysis of
evolved results shows multiple parallels to natural systems, such as
differentiation in constituent members of an aggregate, and life-like, complex
ecosystems.
The fundamental
challenge faced by any visual system within natural environments is the
ambiguity caused by the fact that light that falls on the system's sensors
conflates multiple attributes of the physical world. Understanding the
computational principles by which natural systems overcome this challenge and
generate useful behaviour remains the key objective in neuroscience and machine
vision research. In this paper we introduce Mosaic World, an artificial life
model that maintains the essential characteristics of natural visual ecologies,
and which is populated by virtual agents that – through ‘natural' selection –
come to resolve stimulus ambiguity by adapting the functional structure of
their visual networks according to the statistical structure of their
ecological experience. Mosaic World therefore presents us with an important tool
for exploring the computational principles by which vision can overcome
stimulus ambiguity and usefully guide behaviour.
This paper
investigates evolvability of artificial neural networks within an artificial
life environment. Five different structural mutations are investigated,
including adaptive evolution, structure duplication, and incremental changes.
The total evolvability indicator, Etotal, and the evolvability function through
time, are calculated in each instance, in addition to other functional
attributes of the system. The results indicate that incremental modifications
to networks, and incorporating an adaptive element into the evolution process
itself, significantly increases neural network evolvability within open-ended
artificial life simulations.
The principle
challenge faced by any color vision system is to contend with the inherent
ambiguity of stimulus information, which represents the interaction between multiple attributes of the world (e.g.,
object reflectance and illumination). How natural systems deal this problem is
not known, though traditional hypotheses are predicated on the idea that vision
represents object reflectance accurately by discounting early in processing the
conflating effects of illumination. Here we test the merits of this general
supposition by confronting bumblebees (Bombus terrestris) with a color discrimination task that can only be
solved if information about the illuminant is not discounted, but maintained in
processing, and thus available to higher-order learned behavior. We show that
bees correctly use the intensity and chromaticity of illumination as a
contextual cue to guide them to different target colors. In fact, we trained
bees to choose opposite – rather than most similar – target colors after an
illumination change. This performance cannot be explained with a simple color
constancy mechanism that discounts illumination. Further tests show that bees
do not use a simple assessment of the overhead illumination, but the spectral
relationships between a floral target and its background. These results
demonstrate that bees can be ‘color constant' without discounting the
illuminant; that in fact they can use the illumination itself as a salient
source of information.
Bees like humans can
continue a surface from its colour even when the scene's global illuminant changes (which is a phenomenon called
colour constancy). It is not known, however, whether they can also generate
colour constant behaviour in more natural complex scenes that are lit by multiple
lights simultaneously,
conditions in which most computational models
of colour constancy fail. To test this, bumblebees were raised in a highly
controlled, yet ecological relevant environment consisting of a matrix of 64
artificial flowers under four spatially distinct lights. As in nature, the bees
had no direct access to information about the illuminants or flowers.
Furthermore, the background of all the flowers in the matrix was black,
independent of illumination. The stimulus information presented to the bee was,
therefore, far more constrained than that normally experienced in nature.
Despite this, the bees learned to identify the rewarded flowers in each
differently illuminated region of the matrix, even when the illumination of one
of the regions was switch with one not previously experienced. These
behavioural results suggest that colour constant behaviour is not resolved by
simply adapting to the global average of spectral stimulus, nor even the
spectral contrast between an object and its immediate surround, but can use behaviorally
relevant contrast relationships between
statistically dependent, but visually distinct stimulus elements of scenes.
We view the world
with two eyes and yet are typically only aware of a single, coherent image.
Arguably the simplest explanation
for this is that the visual system unites the two monocular stimuli into a
common stream that eventually
leads to a single coherent sensation. However, this notion is inconsistent with
the well known phenomenon of rivalry; when physically different stimuli project
to the same retinal location, the ensuing perception alternates between the two
monocular views in space and time [2]. Although fundamental for understanding
the principles of binocular vision and visual awareness, the mechanisms
underlying binocular rivalry remain controversial. Specifically, there is
uncertainty about what determines Physically Identical Targets whether
monocular images undergo fusion or rivalry. By taking advantage of the
perceptual phenomenon of color contrast, we show that physically identical monocular
stimuli tend to rival—not fuse when they signify different objects at the same
location in visual space. Conversely, when physically different monocular
stimuli are likely to represent the same object at the same location in space,
fusion is more likely to result. The data suggest that what competes for visual
awareness in the two eyes is not the physical similarity between images but the
similarity in their perceptual/empirical meaning.
Recent findings show
that colour processing, like most other sensory attributes, is shaped by experience. While
such studies can reveal the mechanisms of development, can they also
help uncover the mechanisms of perception?
Surface perception is
fundamental to human vision, yet most studies of visual cortex have focused on
the processing of borders. We therefore investigated the responses of human
visual cortex to parametric changes in the luminance of uniform surfaces by
using functional MRI. Early visual areas V1 and V2V3 showed
strong and reliable increases in signal for both increments and decrements in surface
luminance. Responses were significantly larger for decrements than for
increments, which was fully accounted for by differences in retinal
illumination arising from asymmetric pupil dynamics. Responses to both
sustained and transient changes of illumination were transient. Signals in
early visual cortex scaled linearly with the magnitude of change in retinal
illumination, as did subjects' subjective ratings of the perceived brightness
of the stimuli. Our findings show that early visual cortex responds strongly to
surfaces and that perception of surface brightness is compatible with brain
responses at the earliest cortical stages of processing. Thus, there could be
import ant interactions between regions representing the surface and those
representing the border. To ensure that the responses we measured were caused
by the local surface alone rather than remote contours, we studied the
represent action of parts of a surface that were separated from the closest
contour by at least 5°. This distance is beyond the influence of boundary
processing that has been measured in human V1, V2, and V3.
There is evidence
that developing thalamic cells become dependent for their survival on the
integrity of their afferent and/or efferent connections, which may
provide required levels of neural activity and/or essential neurotrophic
factors. These connections develop in the second half of gestation in
mice and, during this time (embryonic days 17–19), isolated thalamic cells
either grown as explants or dissociated from each other lose their
ability to survive. Here we show that the loss of viability of explants, but
not of dissociated cells, is delayed if the cultures are treated
with depolarizing stimuli. The survival of dissociated thalamic cells is
promoted by culture medium conditioned by thalamic explants grown
with depolarizing stimuli, indicating that the effect of depolarization
involves trophic factors released by thalamic cells. This survival
promoting effect is found prenatally, but not postnatally, and is prevented by
the neurotrophin blocker K252a. Culture medium conditioned by
cortex also promotes the survival of thalamic cells and this effect does occur
postnatally. These findings suggest that diffusible factors, possibly members of the
neurotrophin family, and depolarizing stimuli regulate thalamic cell survival
before birth, but trophic support from cortex becomes crucial after birth. This
culture model may provide a means of investigating the mechanisms of thalamic
cell survival during development.
This book describes an
empirical theory for why we see illusions.
The colors perceived
by humans in response to light stimuli are generally described in terms of four
color categories (reds, greens, blues and yellows), the members of which are
systematically arrayed around gray. This broadly accepted description of color
sensation differs fundamentally from the light that induces it, which is neither
‘circular' nor categorical. What, then, accounts for these discrepancies
between the structure of color experience and the physical reality that
underlies it? We suggest that these differences are based on two related
requirements for successful color vision:(1) that spectra be ordered according
to their physical similarities and differences; and (2) that this ordering be
constrained by the four-color map problem.
Many neurons die as
the normal brain develops. How this is regulated and whether the mechanism
involves neurotrophic molecules from target cells are unknown. We found that
cultured neurons from a key forebrain structure, the dorsal thalamus, develop a
need for survival factors including brain-derived neurotrophic factor (BDNF )
from their major target, the cerebral cortex, at the age at which they
innervate it. Experiments in vivo have shown that rates of dorsal thalamic cell death are reduced by
increasing cortical levels of BDNF and are increased in mutant mice lacking
functional BDNF receptors or thalamocortical projections; these experiments
have also shown that an increase in the rates of dorsal thalamic cell death can
be achieved by blocking BDNF in the cortex. We suggest that the onset of a
requirement for cortex-derived neurotrophic factors initiates a competitive
mechanism regulating programmed cell death among dorsal thalamic neurons.
The perceived
difference in brightness between elements of a patterned target is diminished
when the target is embedded in a similar surround of higher luminance contrast
(the Chubb illusion). Here we show that this puzzling effect can be explained
by the degree to which imperfect transmittance is likely to have affected the
light that reaches the eye. These observations indicate that this 'illusion' is
yet another signature of the fundamentally empirical strategy of visual
perception, in this case generated by the typical influence of transmittance on
inherently ambiguous stimuli.
Four different colors are needed to make maps t hat avoid adjacent countries of the same color. Because the retinal i mage is two dimensional, like a map, four dimensions of chromatic experience would also be needed to optimally distinguish therefore suggest that the organization of human color vision according to four-color classes (reds, greens, blues, and yellows) has arisen as a solution to this logical requirement in topology.
Although it has long
been apparent that observers tend to overestimate the magnitude of acute angles
and underestimate obtuse ones, there is no consensus about why such distortions
are seen. Geometrical modeling combined with psychophysical testing of human
subjects indicates that these misperceptions are the result of an empirical
strategy that resolves the inherent ambiguity of angular stimuli by generating
percepts of the past significance of the stimulus rather than the geometry of
its retinal projection.
For reasons not well
understood, the color of a surface can appear quite different when placed in
different chromatic surrounds. Here we explore the possibility that these color
contrast effects are generated according to what the same or similar stimuli
have turned out to signify in the past about the physical relationships between
reflectance, illumination, and the spectral returns they produce. This
hypothesis was evaluated by (i) comparing the physical relationships of
reflectances, illuminants, and spectral returns with the perceptual
phenomenology of color contrast and (ii) testing whether perceptions of color
contrast are predictably changed by altering the probabilities of the possible
sources of the stimulus. The results we describe are consistent with a wholly empirical
explanation of color contrast effects.
If Mach bands
arise as an empirical consequence of real-world luminance profiles, several
predictions follow. First, the appearance of Mach bands should accord with
the appearance of naturally occurring high-lights and lowlights. Second, altering
the slope of an ambiguous luminance gradient so that it corresponds more closely
to gradients that are typically adorned with luminance maxima and minima in the
position of Mach bands should enhance the illusion. Third, altering a
luminance gradient so that it corresponds more closely to gradients that
normally lack luminance maxima and minima in the position of Mach bands
should diminish the salience of the illusion. Four, the perception of Mach bands
elicited by the same luminance gradient should be changed by contextual cues that
indicate whether the gradient is more or less likely to signify a curved
or a f lat surface. Because each of these predictions is met, we conclude that Mach
bands arise because the association elicited by the stimulus (the percept) incorporates
these features as a result of past experience.
Mach bands, the illusory brightness maxima and minima perceived at the initiation and termination of luminance gradients, respectively, are generally considered a direct perceptual manifestation of lateral inhibitory interactions among retinal or other lower order visual neurons. Here we examine an alternative explanation, namely that Mach bands arise as a consequence of real-world luminance gradients. In this first of two companion papers, we analyze the natural sources of luminance gradients, demonstrating that real-world gradients arising from curved surfaces are ordinarily adorned by photometric highlights and lowlights in the position of the illusory bands. The prevalence of such gradients provides an empirical basis for the generation of this perceptual phenomenon.
A long-standing
puzzle in vision is the assignment of illusory brightness values to visual
territories based on the characteristics of their edges (the
Craik–O'Brien–Cornsweet effect). Here we show that the perception of the
equiluminant territories flanking the Cornsweet edge varies according to whether
these regions are more likely to be similarly illuminated surfaces having the
same material properties or unequally illuminated surfaces with different
properties. Thus, if the likelihood is increased that these territories are
surfaces with similar reflectance properties under the same illuminant, the
Craik–O'Brien–Cornsweet effect is diminished; conversely, if the likelihood is increased
that the adjoining territories are differently reflective surfaces receiving
different amounts of illumination, the effect is enhanced. These findings
indicate that the Craik–O'Brien–Cornsweet effect is determined by the relative
probabilities of the possible sources of the luminance profiles in the stimulus.
Observation of human
subjects shows that the spectral returns of equiluminant colored surrounds govern
the apparent brightness of achromatic test targets. The influence of color on
brightness provides further evidence that perceptions of luminance are
generated according to the empirical frequency of the possible sources of
visual stimuli, and suggests a novel way of understanding color contrast and
constancy.